Forensic weather system

ABSTRACT

A forensic weather analyzer compares actual meteorological readings with data from multiple weather models. The data is compared and a forensic weather model is selected as the weather model that most closely matches the meteorological readings. The forensic weather model is then used to provide meteorological information pertaining to a weather event such as a hurricane, at a specific location such as a street address.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. provisional patent applicationSer. No. 62/238,206 filed on Oct. 7, 2015, and U.S. provisional patentapplication Ser. No. 62/302,921 filed Mar. 3, 2016, the contents of bothof which are incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates generally to meteorology, and moreparticularly, to a forensic weather system.

BACKGROUND

Severe weather has the potential to cause property damage, economicdamage, and most significantly, loss of life. A variety of severeweather phenomena exist. The types of severe weather phenomena that maybe encountered depend on various factors such as latitude, altitude,topography, and other atmospheric conditions.

Severe weather may include hurricanes. Hurricanes are large, rapidlyrotating storm systems comprising a low-pressure center, and areaccompanied by strong winds, thunderstorms, heavy rain, and often causea storm surge. Each of these elements may produce property damage, landerosion, and other undesirable effects. Hurricanes can occur all overthe world, including in the United States, Hawaii and Puerto Rico.

Severe weather may also include “nor'easters”. Nor'easters are largestorms occurring along the east coast of the United States and AtlanticCanada. Nor'easters cause strong northeast-to-southwest winds that canoften cause property damage. Additionally, similar to hurricanes,nor'easters can cause coastal flooding and coastal erosion, alsoresulting in significant property damage.

A coastal storm brings the risk of a storm surge. A storm surge involvesa rising of water above normal levels due to the storm. The combinationof strong inbound winds and low pressure can cause sea water to reachresidential and commercial areas along the coast. Additional factorssuch as tides and rainfall may also impact the overall flooding causedby a storm surge.

Forecasting of such storms has improved to provide notice to populationsto allow for preparation and evacuations. Nevertheless, powerful stormssuch as hurricanes, typhoons, nor'easters, and cyclones will continue toimpact modern life.

SUMMARY

A forensic weather analyzer compares actual meteorological readings withdata from multiple weather models. The data is compared and a forensicweather model is selected as the weather model that most closely matchesthe meteorological readings. The forensic weather model is then used toprovide meteorological information pertaining to a weather event such asa hurricane, at a specific location such as a street address.

In some embodiments, a system comprising at least one anemometer; atleast one computing device, the at least one computing device comprisinga processor, and a memory coupled to said processor, wherein the memorycontains instructions, that when executed by the processor, perform thesteps of: receiving temporal event data; receiving a subject location;generating a measurement region based on the subject location; andreceiving a plurality of meteorological measurements that are collectedwithin the measurement region; determining a station location for eachof the plurality of meteorological measurements; retrieving an estimateddata value from each of a plurality of weather models for each stationlocation based on the temporal event data; for each station location,computing a difference between a received meteorological measurement anda received estimated data value for each of the plurality of weathermodels; and selecting a weather model from the plurality of weathermodels as a forensic weather model based on the computed differences.

In some embodiments, a method comprising: receiving temporal event data;receiving a subject location; generating a measurement region based onthe subject location; and receiving a plurality of meteorologicalmeasurements that are collected within the measurement region;determining a station location for each of the plurality ofmeteorological measurements; retrieving an estimated data value fromeach of a plurality of weather models for each station location based onthe temporal event data; for each station location, computing adifference between the meteorological measurement and estimated datavalue for each of the plurality of weather models; and selecting aweather model from the plurality of weather models as a forensic weathermodel based on the computed differences.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments of thepresent teachings and together with the description, serve to explainthe principles of the present teachings.

The drawings are not necessarily to scale. The drawings are merelyrepresentations, not necessarily intended to portray specific parametersof the invention. The drawings are intended to depict only exampleembodiments of the invention, and therefore should not be considered aslimiting in scope. In the drawings, like numbering may represent likeelements. Furthermore, certain elements in some of the figures may beomitted, or illustrated not-to-scale, for illustrative clarity.

FIG. 1 is a system in accordance with embodiments of the presentinvention.

FIG. 2 shows details of a weather station.

FIG. 3 shows a map indicating a subject location.

FIG. 4 shows a map indicating a measurement region.

FIG. 5 shows a map indicating a secondary measurement region.

FIG. 6 shows a map indicating a geological exclusion zone.

FIG. 7 shows another example of a map indicating a subject location.

FIG. 8 shows another example of a map indicating a geological exclusionzone.

FIG. 9 shows tables of data for weather stations.

FIG. 10 shows a table of data for weather models.

FIG. 11 is a flowchart indicating process steps for embodiments of thepresent invention.

FIG. 12 is a flowchart indicating process steps for receiving temporaldata.

FIG. 13 is a flowchart indicating process steps for receiving a subjectlocation.

FIG. 14 is a flowchart indicating process steps for generating ameasurement region.

FIG. 15 is a flowchart indicating process steps for selecting a forensicweather model.

FIG. 16 is a flowchart indicating process steps for generating aforensic weather report.

FIG. 17 is a plot from an exemplary forensic weather report.

FIG. 18 is a report summary from an exemplary forensic weather report.

FIG. 19 is an exemplary user interface for forensic weather reportgeneration options.

DETAILED DESCRIPTION

Hurricanes, tropical storms, and cyclones can be among the deadliest andmost destructive peril because the very strong winds and stormsurge/tide that are often associated with them for long periods of time.In 2012, Hurricane Sandy caused an estimated $71.4 billion in damage.Some of that damage was caused by winds and some damage was caused bythe storm surge and tides. The biggest question among professionalsinvolved in determining and resolving insurance claims is: Which weatherphenomena likely caused the damage and how much damage did each of theweather phenomena cause? When an insurance carrier, for instance,receives a claim, the adjuster they send out is tasked with determiningcausation. That is, it is desirable to determine if the damage wascaused by wind speeds, the storm surge, or both. Additional questionsarise when a structure has a high dollar loss or one in which thewindows, or entire structure is destroyed. Knowing the wind speeds isalso very important for investigations when structures of windows arerated to withstand a certain wind speed. When an adjuster, engineer,attorney or others search for weather records from the official weatherstations, they are often faced with many problems. Examples include thefollowing:

-   -   1. National Oceanic and Atmospheric Administration (NOAA)        surface wind observations are often far from the subject        location, sometimes 15-25 miles away. For a scenario involving        Hurricane Sandy along the coastline in Barnegat, N.J., the        closest NOAA wind observation station was located 23.1 miles        away and was approximately 15.8 miles inland. There were no        easily identifiable wind records in the area.    -   2. The weather stations are often located well inland and are        not representative of the winds closer to or along the coastline        where stronger winds may exist.    -   3. These weather stations are often far away from the most        intense part of the storm.    -   4. Weather stations often have electricity and communication        failures, with the result that meteorological observations may        not be taken or recorded.    -   5. Observers at airports often evacuate and close down their        posts, which in doing so stops the recording of wind        observations.

Thus, there remains a major void in accurately determining what thesustained wind speeds, wind gusts and storm surge heights were during ahurricane, tropical storm or other significant windstorm. Not only isthis true for peak values but also for the timing of these values. Thisbecomes very important when trying to determine the causation of theloss, the value of the loss, and who ultimately is responsible forpaying for the damage. Overwhelmingly, the question comes down towhether an insurance company or federal government (National FloodInsurance Program) is responsible for payment, and how much. However, ifthe winds were never strong enough to cause damage to the structure,then there may be a denial or dispute in coverage. Not only was this amulti-billion dollar question during “Superstorm Sandy,” but it was evenmore of an unknown during Hurricane “Katrina” in 2005. The results havemajor implications on the home and business owners, the insurancecarriers, and the federal government.

To address the aforementioned problems, embodiments of the presentinvention provide a back-end machine process that provides a localizedmeteorological estimation including detailed hour-by-hour wind, weather,and storm surge conditions for a specific, user-supplied subjectlocation. Embodiments include archiving of the 00-hour initializationsof several computer models every hour, and saving them to a serverand/or database, enabling a post-storm verification process.

These computer models estimate numerous meteorological values andmeasurements and provide a detailed ‘snapshot’ of the weatherconditions, at the ground and well above the ground, for the entireUnited States, Hawaii, and Puerto Rico. In addition to the weathermodels, embodiments also incorporate data from the “SLOSH” (“Sea, Lake,and Overland Surges from Hurricane”) computer model in order todetermine and calculate storm surge heights based on specific storminformation and criteria.

In embodiments, a user can visit a website, enter the address,altitude/longitude pertaining to a subject location, or their currentGPS location and view a map to verify the correct location. The user canadjust the location on the mapping program manually to the correctlocation. The user may be prompted to enter their payment information.As they are doing this, a forensic weather analyzer in accordance withembodiments of the present invention reviews the archives and extractsdata for the requested subject location. The end result is an automated,forensic weather report that contains hour-by-hour wind speeds, windgusts, wind directions, barometric pressures and storm surge heights, aswell as graphs of the wind and storm surge data, an a color-contouredimage of the wind gusts when the maximum wind gusts were achieved and animage of the storm surge height when the maximum storm surge occurred ateach specific location that was queried. Thus, the forensic weatherreport provides a homeowner, insurance adjuster, or other stakeholderwith critical information regarding the weather event. Additionaldetails of embodiments are further explained in the followingparagraphs.

Reference throughout this specification to “one embodiment,” “anembodiment,” “some embodiments”, or similar language means that aparticular feature, structure, or characteristic described in connectionwith the embodiment is included in at least one embodiment of thepresent invention. Thus, appearances of the phrases “in one embodiment,”“in an embodiment,” “in some embodiments”, and similar languagethroughout this specification may, but do not necessarily, all refer tothe same embodiment.

Moreover, the described features, structures, or characteristics of theinvention may be combined in any suitable manner in one or moreembodiments. It will be apparent to those skilled in the art thatvarious modifications and variations can be made to the presentinvention without departing from the spirit and scope and purpose of theinvention. Thus, it is intended that the present invention cover themodifications and variations of this invention provided they come withinthe scope of the appended claims and their equivalents. Reference willnow be made in detail to the preferred embodiments of the invention.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of this disclosure.As used herein, the singular forms “a”, “an”, and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. Furthermore, the use of the terms “a”, “an”, etc., do notdenote a limitation of quantity, but rather denote the presence of atleast one of the referenced items. The term “set” is intended to mean aquantity of at least one. It will be further understood that the terms“comprises” and/or “comprising”, or “includes” and/or “including”, or“has” and/or “having”, when used in this specification, specify thepresence of stated features, regions, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, regions, integers, steps,operations, elements, components, and/or groups thereof.

FIG. 1 is a diagram 100 of a forensic weather system in accordance withembodiments of the present invention. Forensic weather analyzer 102 maybe embodied in a computer comprising a processor 104, a memory 106coupled to the processor 104, a user interface 108, and a communicationinterface 110. In embodiments, multiple processors and/or cores may beused. In some embodiments, the forensic weather analyzer 102 may be adistributed computer system. The memory 106 may include a non-volatilememory, such as ROM, flash memory, optical storage, magnetic storage,magnetic hard disks, solid state hard disks, or other suitabletechnology. In embodiments, the memory 106 may include memory, which isnot a transitory signal per se. The user interface 108 may include, butis not limited to, a display, a keyboard, a mouse, a touchscreen, and/orvoice directed guidance. The communication interface 110 may include awired Ethernet port, a Wi-Fi interface, cellular interface, or othersuitable interface for communication over network 112. In embodiments,network 112 includes the Internet. In alternative embodiments, network112 may include a wide area network (WAN), local area network (LAN),cloud network, cellular network, Wi-Fi network, Bluetooth network,and/or another suitable network.

Using communication interface 110, the forensic weather analyzer 102 maycommunicate with multiple computerized weather stations, indicated asreferences 114, 116, and 118. While three weather stations areillustrated in FIG. 1, in practice many more (or fewer) weather stationsmay be utilized. Each weather station comprises hardware to providemeteorological measurements to the forensic weather analyzer 102. Insome embodiments, the meteorological measurements may be providedunsolicited by the weather stations. In other embodiments, themeteorological measurements may be provided in response to a query fromthe forensic weather analyzer 102. In some embodiments, a combination ofqueried and unsolicited measurements may be used.

In embodiments, the forensic weather analyzer 102 may interact with aclient device 126. The client device may render a user interface thataccepts data to be entered into fields of a web page. The data may thenbe communicated to the forensic weather analyzer 102 via network 112.The data may include temporal event data, which pertains to a particulartime or time interval of interest. The data may also include a subjectlocation, which pertains to a location for which forensic weather datais desired. Thus, a user may be able to access a web page via the clientdevice 126, and enter the pertinent information to enable generation ofa forensic weather report by the forensic weather analyzer 102. Theclient device 126 includes a communication interface to enablecommunication over network 112. In embodiments, the client device 126may include a PC, a laptop, a tablet computer, or a mobile phone. Insome embodiments, the client device may include a camera, and/or asatellite positioning receiver such as a Global Positioning System (GPS)receiver.

The forensic weather analyzer 102 may interact with multiple weathermodels, indicated as references 120, 122, and 124. While three weathermodels are illustrated in FIG. 1, in practice many more weather modelsmay be utilized. The weather models may be implemented on computers thatprovide application programming interfaces (APIs) and/or othermechanisms such as Simple Object Access Protocol (SOAP), and/or JSON(JavaScript Object Notation) to enable model data to be retrieved by theforensic weather analyzer 102. Data may be exchanged in a variety offormats, including, but not limited to, Extensible Markup Language XML,GRIB (Gridded Binary), and or CDF (Common Data Format).

Weather models 120, 122, and 124 may include, but are not limited to, arapid refresh model (RR), a High Resolution Rapid Refresh (HRRR) model,and/or a Real Time Meso Analysis (RTMA) model. Additionally, one of themodels may be a Sea, Lake, and Overland Surges from Hurricanes (SLOSH)model used for estimating storm surge at a given time (or time window)and subject location.

The Rapid Refresh (RR) numerical weather is operated by the NationalCenters for Environmental Prediction (NCEP). The RR runs with twoversions. The first generates weather data on a 13-km (8-mile)resolution horizontal grid, and the second, the High-Resolution RapidRefresh (HRRR), generates data down to a 3-km (2-mile) resolution gridfor smaller regions of interest. RR forecasts are generated every hourwith forecast lengths going out 18 hours. Multiple data sources go intothe generation of RR forecasts, including, but not limited to,commercial aircraft weather data, balloon data, radar data, surfaceobservations, and satellite data.

The Real-Time Mesoscale Analysis (RTMA) is a National Oceanic andAtmospheric Administration (NOAA)/NCEP high-spatial and temporalresolution analysis/assimilation system for near-surface weatherconditions. Its main component is the NCEP/EMC Gridpoint StatisticalInterpolation (GSI) system applied in two-dimensional variation mode toassimilate conventional and satellite-derived observations.

The RTMA provides field forecasters with high quality analyses fornowcasting, situational awareness, and forecast verification purposes.The system currently produces hourly analyses at 5 km and 2.5 kmresolution for the Conus NDFD grid, 6 km for the Alaska NDFD grid and2.5 km for the Hawaii, Puerto-Rico, and Guam NDFD grids.

The Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model is acomputerized numerical model developed by the National Weather Service(NWS) to estimate storm surge heights resulting from historical,hypothetical, or predicted hurricanes by taking into account theatmospheric pressure, size, forward speed, and track data. Theseparameters are used to create a model of the wind field which drives thestorm surge.

The SLOSH model includes a set of physics equations which are applied toa specific locale's shoreline, incorporating the unique bay and riverconfigurations, water depths, bridges, roads, levees, and other physicalfeatures.

The SLOSH model may utilize multiple techniques, alone or incombination, to estimate a surge:

Deterministic Approach—Forecasts surge based on solving physicsequations. This approach uses a single simulation based off of a“perfect” forecast which results in a strong dependence on accuratemeteorological input. The location and timing of a hurricane's landfallis crucial in determining which areas will be inundated by the stormsurge. Small changes in track, intensity, size, forward speed, andlandfall location can have huge impacts on storm surge.

Probabilistic Approach—The Probabilistic Surge (P-Surge) productincorporates statistics of past forecast performances to generate anensemble of SLOSH runs based on distributions of cross track, alongtrack, intensity, and size errors. The latest version explicitly modelsthe astronomical tide.

Composite Approach—Predicts surge by running SLOSH several thousandtimes with hypothetical hurricanes under different storm conditions. Theproducts generated from this approach are the Maximum Envelopes of Water(MEOWs) and the Maximum of MEOWs (MOMs) which is regarded by theNational Hurricane Center (NHC) as the best approach for determiningstorm surge vulnerability for an area since it takes into accountforecast uncertainty. The MEOWs and MOMs play an integral role inemergency management as they form the basis for the development of thenation's evacuation zones.

The data from weather models 120, 122, and 124 may be hosted on one ormore data servers. Data, in a format such as, without limitation, GRIB,GRIB2, XML, or CDF, may be accessed by forensic weather analyzer 102 vianetwork 112. The GRIB standard is well suited for transmitting largevolumes of gridded data to data centers over high-speedtelecommunication lines using modern protocols. By packing informationinto the GRIB code, messages can be made more compact than characteroriented bulletins, which enables faster computer-to-computertransmissions. GRIB can also serve as a data storage format, generatingthe same efficiencies relative to information storage and retrievaldevices.

A GRIB2 message includes multiple parameters with values located at anarray of grid points or represented as a set of spectral coefficients.Logical divisions of the message are designated as sections, each ofwhich provides control information and/or data. There are multiplesections within a message. An indicator section includes itsidentification as a GRIB message, and includes a message length. Anidentification section includes a section length, section number, andcharacteristics that apply to all processed data in the GRIB message. Anoptional local use section contains additional items for local use byoriginating centers. A Grid Definition Section includes a definition ofgrid surface and geometry of data values within the surface. A ProductDefinition Section includes a description of the nature of the data. AData Representation Section includes a description of how the datavalues are represented. A Bit-Map section includes an indication ofpresence or absence of data at each grid point, as applicable.

A sample use case of embodiments of the present invention may include auser desiring to obtain forensic weather information for a given subjectlocation and event window. For example, the user may wish to obtaininformation about sustained winds at a particular address during arecent hurricane. The user enters subject location information and theevent window of interest via client device 126. The information istransmitted to forensic weather analyzer 102 via network 112. Theforensic weather analyzer retrieves actual meteorological measurementsfrom one or more of the weather stations (114, 116, and/or 118). Themeteorological measurements may have been taken at some distance (e.g.,several miles) from the subject location, and thus may not berepresentative of conditions experienced at the subject location. Thus,the forensic weather analyzer 102 compares the meteorologicalmeasurements against simulated data from the plurality of weather models120, 122, and/or 124. The forensic weather analyzer 102 selects theweather model that most accurately aligns with the meteorologicalmeasurements from one or more of the weather stations (114, 116, and/or118). This selected weather model is the forensic weather model. Theforensic weather model is then queried for the given time frame (eventwindow) at the given subject location, or within the closest resolutionprovided by the forensic weather model. The data from the forensicweather model is then formatted into a report with textual andvisual/graphical information and provided to the user from the forensicweather analyzer 102 to the client device 126 via network 112. Someembodiments include the forensic weather analyzer and at least oneweather station, where the weather station includes at least oneanemometer. In this way, the user can receive an assessment ofconditions at the subject location during the time period of interest.

FIG. 2 shows details of a weather station 200 in accordance withembodiments of the present invention. Weather station 200 comprises aprocessor 202, coupled to a memory 204. In embodiments, multipleprocessors and/or cores may be used. In some embodiments, the weatherstation 200 may include a distributed computer system. The memory 204may include a non-volatile memory such as ROM, flash memory, opticalstorage, magnetic storage, magnetic hard disks, solid state hard disks,or other suitable technology. In embodiments, the memory 204 is not atransitory signal per se. The weather station 200 further comprises aninput/output (I/O) interface 206 to receive inputs from variousmeteorological instruments. Each instrument outputs a digital and/oranalog signal that is read by the processor via the I/O interface. Themeteorological instruments may include, but are not limited to, ananemometer 218, a thermometer 210, a barometer 212, a hygrometer 214, arain (precipitation) gauge 220, a wind direction indicator 216, and/orany other suitable tools. Weather station 200 may further comprise anantenna 222 to facilitate wireless communication via a cellular network,Wi-Fi network, Bluetooth network, the Internet and/or other suitablenetwork such that data from weather station 200 can be transmitted vianetwork 112 (FIG. 1) to forensic weather analyzer 102 (FIG. 1). Weatherstation 200 may be similar to weather stations 114, 116, and 118illustrated in FIG. 1.

The hygrometer 214 may be used for measuring the moisture content in theatmosphere. Temperature data may be provided by thermometer 210.Barometric pressure may be provided by barometer 212. Rainfall amountsmay be provided by rain gauge 220. Wind speed may be provided byanemometer 218, and wind direction may be provided by wind directionindicator 216. In embodiments, each weather station stores a certainamount of history in its memory 204. The history (recordedmeteorological measurements) may be periodically retrieved by theforensic weather analyzer 102 (FIG. 1), for long term storage on theforensic weather analyzer or other networked storage or database.

FIG. 3 shows diagram 300 of a map 302 indicating a subject location 304.In this example, the subject location 304 is on the coast of southernNew Jersey. Diagram 300 further shows a flow of information for startinga forensic weather analysis. The flow includes establishing a time ofinterest 306, which forms the basis of an event window. For example, thetime of interest may be interpreted as a start time for a five hour timewindow. In other embodiments, a user may specify the time of interestalong with a desired time window. In such embodiments, a user mayspecify a time window of 12 hours, 24 hours, or other suitable intervalcorresponding to a weather event. The flow may continue with convertingthe time of interest to an epoch start time 308. The epoch start timemay be in the form of GPS seconds, UNIX seconds, UTC time, or othersuitable format. In this way, the event time is independent of time zoneand daylight savings time settings. The flow may continue with enteringa street address 310 of a subject location. The flow may then continuewith converting the street address 310 to a latitude-longitudecoordinate pair 312.

FIG. 4 shows diagram 400 of a map 402 indicating a measurement region406. The measurement region 406 may be defined such that the subjectlocation 404 is centered within the measurement region 406. Themeasurement region 406 encompasses multiple weather stations indicatedas 408, 410, and 412. Each weather station may be similar to weatherstation 200 shown in FIG. 2. The forensic weather analyzer 102 (FIG. 1)utilizes measurements from weather stations within the measurementregion 406 to perform an assessment of various weather models in orderto select a forensic weather model.

FIG. 5 shows diagram 500 of a map 502 indicating a secondary measurementregion in accordance with alternative embodiments. In some embodiments,a default measurement region 506 may be selected, based on the subjectlocation 504. For example, the default measurement region 506 may bedefined by a circle of a predetermined radius (e.g. 5 miles). In somecases, there may only be few weather stations (508, 510, and 512) in thedefault measurement region 506. In such cases, the forensic weatheranalyzer 102 (FIG. 1) may establish a secondary measurement region 514in order to encompass additional weather stations, such as 516. A highernumber of weather stations within the measurement region allow morecomparisons between model data and real-world meteorologicalmeasurements. In some embodiments, rather than establishing a secondarymeasurement region, the default measurement region 506 may be expandedin radius until a predetermined number of weather stations are includedor a maximum radius is reached. For example, in one embodiment, theforensic weather analyzer is configured to start with a 3 mile radiusdefault region, and attempts to encompass at least 5 weather stations.If there are fewer than 5 weather stations, the radius is incrementallyincreased (e.g. at mile increments). Each time the radius is increased,a count of included weather stations is made. If the number of weatherstations reaches 5, the process is complete. If the number of weatherstations is still fewer than 5, the radius increases up to a maximumvalue (e.g. 30 miles). Once the radius reaches its maximum value, ituses the number of weather stations included in the measurement regionfor input data used in making a selection of a forensic weather model.In embodiments, the processor 104 of the forensic weather analyzer 102executes instructions in memory 106 to perform the aforementionedprocess of expanding the measurement region.

FIG. 6 shows diagram 600 of a map 602 indicating a geological exclusionzone 620. In this example, the measurement region 614 is reshaped basedon an intersection with a geological boundary. In this case, thegeological boundary is the boundary between the land of New Jersey andthe Atlantic Ocean. In certain cases, it may be desirable to discarddata from certain weather stations within the measurement region. Forexample, referring again to FIG. 5, weather stations 508, 510, and 516are land-based weather stations, while weather station 512 is on theocean (e.g. located on a buoy, oil rig, or other ocean-based structure).In some embodiments, a geological exclusion zone 620 can be establishedalong the boundary of a geological feature (e.g. an ocean, lake,mountain range, etc. . . . ) to eliminate weather stations within theexclusion zone. For example, wind speeds may be much higher over an openocean than on land. Thus, even if an ocean-based weather station iswithin the measurement region, it may be better to discard its dataunder certain conditions. Thus, in FIG. 6, only weather stations 610,608, and 616 are used in determining the forensic weather model. Inembodiments, the processor 104 of the forensic weather analyzer 102utilizes a database of geological features to determine if there isoverlap between a geological feature and a measurement region. If thereis overlap, a user may be provided with an option to create a geologicalexclusion zone for those feature(s).

FIG. 7 shows diagram 700 including another example of a map 702indicating a subject location 704. In this case, the subject location iswithin Colorado. A measurement region 706 includes three weatherstations, indicated as 708, 710, and 712. Colorado has a wide variety ofelevations, as it has plains and mountain ranges. In some embodiments, ageological exclusion zone may be utilized to remove weather stations athigher elevations. For example, suppose that the subject location 704 isat a 5000 foot elevation (above sea level), and weather station 708 isat 8000 feet above sea level, weather station 710 is at 5300 feet abovesea level, and weather station 712 is at 4900 feet above sea level. Insuch a scenario, wind and temperature data may vary considerably betweenthe subject location and the weather station 708. In such a case, amodel that closely matches the weather station 708 may not necessarilybe a close match for the other stations. Thus, an exclusion zone can beused to remove weather station 708.

FIG. 8 shows diagram 800 another example of a map 802 indicating ageological exclusion zone 828 for a measurement region 806 centeredaround a subject location 804. The geological exclusion zone 828 removesa weather station from consideration when determining a forensic weathermodel. As shown in FIG. 8, weather stations 810 and 812 are used fordetermining the forensic weather model, whereas there is no weatherstation in FIG. 8 corresponding to weather station 708 of FIG. 7, due tothe geological exclusion zone 828. In embodiments, the processor 104 ofthe forensic weather analyzer 102 compares elevations of the subjectlocation and each weather station, and may generate an exclusion zonefor weather stations located at an elevation that exceeds apredetermined threshold from the subject location. In the example above,with a threshold of plus or minus 1500 feet, weather stations 710 and712 are included, while weather station 708 is excluded. Thus, with thegeological exclusion zone 828, weather station 708 is not used indetermining the forensic weather model.

FIG. 9 shows a set of tables 900 for weather stations. Table set 900 maybe stored within memory 106 of forensic weather analyzer 102. Table set900 includes station table 901. Station table 901 may include a stationidentifier field 902, which provides an alpha-numeric identifier for agiven weather station. The table 901 may include a location field 904,which indicates a location for a given weather station. In embodiments,the location may be represented in a latitude-longitude format. Thetable 901 may include an elevation field 906, which provides thealtitude of the weather station with respect to sea level. The table 901contains basic weather station information. In embodiments, the table901 may be implemented in a relational database format, in which thedata is accessible through structured query language (SQL) commands. Thetable 901 may be linked to meteorological measurement tables (922, 930,and 932). A measurement table 922 contains data values for the weatherstation of row 910. The measurement table 922 includes a data valuecolumn 926, and a time column 928. Thus, for each weather station incolumn 902, a corresponding measurement table such as 922 containsmeteorological measurements acquired at a time indicated in column 928(e.g., table 930 for row 912, and table 932 for row 914). Inembodiments, the time value in column 928 is represented in GPS seconds.In embodiments, multiple data value columns may be present in table 922.The data columns may include, but are not limited to, temperature data,sustained wind speed data, peak wind gust speed data, humidity data,rainfall data, and/or average wind direction data, among others.Similarly, the weather station in row 912 has corresponding measurementtable 930, and the weather station in row 914 has correspondingmeasurement table 932. Note that for clarity, not all columns of themeasurement tables are shown. However, each measurement table (922, 930,and 932) may further include additional columns such as stationidentifier, and any other columns needed to allow relational databaseoperations between the tables of table set 900.

In practice, multiple data fields may be recorded for each weatherstation. Furthermore, the multiple data fields may be recorded formultiple times. For example, measurements can be recorded on a periodicinterval (e.g. hourly, or every ten minutes, etc. . . . ). Thus, for agiven time, it is possible to retrieve the meteorological measurementsfrom the weather station at that time. In some embodiments, if a userrequests a time for which no meteorological measurement was recorded,then the processor 104 of the forensic weather analyzer 102 may retrievethe data values acquired at a time nearest to the requested time. Theprocessor 104 may compute a difference between a requested time and anactual time within a time column of a measurement table, and retrievethe measurement with the minimum time difference between requested timeand actual time.

FIG. 10 shows a weather model table 1000. Table 1000 may be storedwithin memory 106 of forensic weather analyzer 102. The table 1000 mayinclude a model identifier field 1002, which provides an alpha-numericidentifier for a given weather model. The table 1000 may include alocation field 1004, which includes a location for which an estimateddata value from the model pertains. The table 1000 may include a datavalue field 1006, which includes a magnitude of an estimated data valuefrom the model. For example, the data value may refer to sustained windspeed, peak wind gust speed, or other meteorological value that iscapable of being estimated by the model for a given time and location.The table 1000 may include a difference field 1008 which includes adifference between an estimated value from a model and a measured valuefrom a weather station. Row 1010 shows information from model 1 at afirst location. Row 1012 shows information from model 2 at a firstlocation. Row 1014 shows information from model 3 at a first location.Row 1016 shows information from model 1 at a second location. Row 1018shows information from model 2 at a second location. Row 1020 showsinformation from model 3 at a second location. In embodiments, multipletables within a relational database may be used to facilitate queries ofdata from multiple weather stations and multiple models at multiplelocations for various times. The differences are used to qualify whichmodel is best for a given circumstance. In the example of FIG. 10, model1 (indicated in row 1010 and 1016) has the smallest absolute value ofthe difference, and thus is the best model. Other embodiments may usemore sophisticated techniques for determining the best model, such asusing weighted constants, and factoring in distance from the subjectlocation.

In some embodiments, a Mean Absolute Error (MEA) is computed for eachmodel over a plurality of weather stations. The MEA may be used toqualify each of the multiple models. Below is an example table of datafor wind error by station, for a given model.

The MEA for multiple models can be computed. Below is an example of MEAvalues for three different models for a storm.

Sustained Gust Wind Sample STATION Wind Error Error Size VAF 5.660 2.54641 VCT 6.662 5.653 42 VKY 6.945 6.945 43 VTI 7.511 7.101 48 XBP 7.7987.599 43

In the example table above, Model 1 is the forensic weather model, as ithas the lowest MEA amongst the three models.

Model MEA Model 1 2.92328085106383 Model 2 3.46315744680851 Model 35.21126808510638

Additionally, embodiments of the present invention may alsoautomatically perform a quality control check on the data returned in aforensic weather report. This feature uses the same observed dataavailable to the model verification step above. The system determinesthe distance from the subject location to each relevant weather station.For stations less than a predetermined distance from the subjectlocation, a distance-weighted average between the estimated data andobserved data is computed. Data extraction dates are storm specific anddetermined by an administrator when creating a weather event, oralternatively can be automated as part of the forensic weather reportgeneration process.

FIG. 11 is a flowchart 1100 indicating process steps for embodiments ofthe present invention. In process step 1150, temporal event data isreceived. This indicates the event window, which is the time period ofinterest for which a forensic weather analysis is to be performed. Inprocess step 1152, a subject location is received. This indicates alocation for which a forensic weather analysis is to be performed. Inprocess step 1154, a measurement region is generated. The measurementregion is a region in which weather stations are queried for actualmeteorological measurements. An example of a measurement region isindicated as 706 in FIG. 7. In process step 1156, meteorologicalmeasurements are received. These measurements may come from weatherstations such as those indicated in FIG. 1 (114, 116, and 118). Inprocess step 1158, estimated data values from a plurality of weathermodels are retrieved. These may include weather models such as thoseindicated in FIGS. 1 (120, 122, and 124). In process step 1160, aforensic weather model is selected from amongst the weather models thatwere used to retrieve data in process step 1158. The forensic weathermodel is the model that best represents the actual conditions of thesubject location over the time period as defined by the event window. Inprocess step 1162, the data for the subject location is extracted fromthe forensic weather model. In process step 1164, a forensic weatherreport is generated. The forensic weather report may include textualand/or graphical information based on data from the forensic weathermodel.

FIG. 12 is a flowchart 1200 indicating process steps for receivingtemporal data. This flowchart shows some additional sub-steps of step1150 of flowchart 1100 of FIG. 11. In process step 1250, an event nameis received. In practice, the event name may be the name of a namedstorm such as a hurricane or winter storm. In embodiments, the forensicweather analyzer 102 contains or has access to a database that includesthe time window for each named storm for a given region. In process step1252, the event name is converted to an epoch time (e.g. number of GPSseconds). In other embodiments, a local time is received in process step1254. Then, in process step 1256, the local time is converted to anepoch time such as GPS seconds, UTC time, Unix seconds, or othersuitable epoch. In process step 1258, an event window is created. Thismay include adding a predetermined time offset before and after theepoch time. For example, in embodiments, the event window is defined asstarting from three hours before the epoch time and ending at threehours after the epoch time.

FIG. 13 is a flowchart 1300 indicating process steps for receiving asubject location. This flowchart shows some additional sub-steps of step1152 of flowchart 1100 of FIG. 11. In embodiments, an address isreceived at step 1350. The address may then be converted to alatitude-longitude coordinate pair in process step 1352. This may beperformed by querying a database containing latitude-longitude pairs foreach address. In process step 1356, the latitude longitude pair isstored as a subject location. Optionally, an elevation is retrieved inprocess step 1354 and also stored as part of the subject locationinformation. Alternatively, instead of receiving a street address, a mappoint may be received in process step 1351. The map point may beselected by a user via a computer, tablet, or other suitable device. Themap point may be converted to a latitude-longitude pair in process step1352. Alternatively, the subject location may originate from receivingsatellite positioning coordinates in process step 1355. For example, asmart phone with an integrated GPS may be used by an insurance adjuster.When the adjuster arrives at the property, a forensic weather reportusing the latitude and longitude recorded by the GPS may be initiated.

FIG. 14 is a flowchart 1400 indicating process steps for generating ameasurement region. This flowchart shows some additional sub-steps ofstep 1154 of flowchart 1100 of FIG. 11. In process step 1450, a regionis created with a subject location at the center of it. In embodiments,the region may have a circular shape with a radius extending from thesubject location to the perimeter of the measurement region. In processstep 1452, a count of weather stations within the region is computed.This may be performed by computing a distance from the location of theweather station to the subject location. If the distance is less thanthe radius of the measurement region, then the weather station is deemedto be within the measurement region. For example, FIG. 4 shows threeweather stations (408, 410, and 412) within the measurement region 406.Embodiments may then proceed to process step 1454, where a check is madeto determine if a sufficient number of weather stations are present. Inthe example of FIG. 4, three weather stations are present. If thepredetermined minimum number of stations is four, then as indicated inthe map of FIG. 4, there are only three weather stations, and thus, thecriterion is not met. If the criterion is not met, then the process mayproceed to step 1460 which includes a secondary region with a subjectlocation at its center. An example of a secondary region is shown as 514in FIG. 5. In this example, the secondary region encompasses anadditional weather station 516 to allow the number of weather stationsto reach the predetermined threshold. If the number of weather stationsis sufficient in process step 1454, then the process continues toprocess step 1456, of applying exclusion zones. An example of such anexclusion zone is shown as 828 in FIG. 8. In that example, the exclusionzone is removing a high-elevation area, to eliminate weather stationshaving an elevation above a predetermined threshold. Another example ofa geological exclusion zone is shown as 620 of FIG. 6. In that example,a geological exclusion zone is formed over the portion of themeasurement region that extends over the Atlantic Ocean. In this way,weather stations over the ocean may be eliminated from the analysis. Insome scenarios, it may be desirable to include the over-ocean weatherstations, whereas in other scenarios it may be desirable to excludethem. The geological exclusion zones allow an operator of the forensicweather analyzer to make such decisions on a case-by-case basis.

FIG. 15 is a flowchart 1500 indicating process steps for selecting aforensic weather model. This flowchart shows some additional sub-stepsof step 1154 of flowchart 1100 of FIG. 11. At process step 1550, foreach weather station, at a given date/time, actual meteorologicalmeasurements are compared with estimated measurements from each weathermodel for that location and date/time. Differences are computed betweenthe actual measurement and the estimated measurements. The magnitude(absolute value of the differences) may be computed. In someembodiments, a plurality of weighting factors may be applied to theweather stations in process step 1552. In some embodiments, eachdifference is applied by a weighting factor K. In embodiments, K is afunction of distance d between the weather station and subject location:K=f(d)

In embodiments, the function may be defined as:(R/d)

Where R=the distance of the radius of the measurement region and d isthe distance from the weather station to the subject location.

Thus, supposing an example with two weather stations W₁ and W₂, where W₁is 2 miles from the subject location, and W₂ is 4 miles from the subjectlocation, and the measurement region radius is 8 miles, then for a givenmodel, the total difference for the model D_(M) may be defined as:D _(M) =KT ₁ +KT ₂

Where T_(X) is the magnitude of difference between the actualmeteorological measurement from weather station W_(X) and the estimatedvalue from weather model M_(X). Substituting the example values yields:D _(M)=4(T ₁)+2(T ₂)

Thus, as can be seen, the formula gives more weight to weather stationcomparisons that are closer to the subject location.

Using the difference values from table 1000 (FIG. 1), the totaldifference for the models shown is:

Total Model # W1 W2 Diff Model 1 0.6 1.5 5.4 Model 2 12.5 11 72 Model 315.2 5.1 71

Note that the absolute values of the differences are shown in the abovetable.

In process step 1554, the model with the lowest magnitude of differenceis selected as the forensic weather mode. Thus, using the aforementionedexample, it can be seen that Model 1 has the lowest total difference,and so Model 1 is selected as the forensic weather model.

In some embodiments, the forensic weather analyzer may includeinstructions stored in memory that compare a sustained wind data pointfrom one or more models at a weather station location with the actualmeasurement from the weather station. The comparison may be used inevaluation of which model is best for modeling a particular weatherevent. In some embodiments, the distance between the weather station andthe subject location is considered in the evaluation. In someembodiments, only weather stations within a predetermined distance fromthe subject location may be considered. In other embodiments, aweighting factor may be used, such that the closer the weather stationis to the subject location, the more weight is used in determining theeffectiveness of the model. The model effectiveness may be determined bycomputing an error between each model and the actual measurement. Theerror may then be weighted as a function of distance from the subjectlocation, or filtered out completely if the weather station is deemed tobe too far from the subject location.

FIG. 16 is a flowchart 1600 indicating process steps for generating aforensic weather report. This flowchart shows some additional sub-stepsof step 1164 of flowchart 1100 of FIG. 11. In process step 1650, a graphof the sustained wind speed as a function of time during the eventwindow is rendered. In process step 1652, a graph of the peak wind speedas a function of time during the event window is rendered. In processstep 1654, a graph of the storm surge as a function of time during theevent window is rendered. In process step 1656, a graph of thetemperature as a function of time during the event window is rendered.In process step 1658, a report summary including textual data indicatingmodeled weather conditions during the event window is displayed.

FIG. 17 is a plot 1700 from an exemplary forensic weather report. Thehorizontal axis 1702 shows time intervals. The vertical axis 1704 is adual value axis. The right side shows storm surge in feet. The left sideof the vertical axis shows wind speed in miles per hour. This allowsoverlay of various modeled data points. Plot 1706 represents a stormsurge as estimated from a SLOSH model. Plot 1708 represents a sustainedwind speed as estimated from the forensic weather model. Plot 1710represents a peak wind gust speed as estimated from the forensic weathermodel. A legend 1712 provides symbol explanations for understanding ofthe plots.

FIG. 18 is a report summary 1800 from an exemplary forensic weatherreport. Field 1802 indicates a subject location for the forensic weatherreport. Field 1804 indicates a start time for the forensic weatherreport. Field 1806 indicates a maximum storm surge for the forensicweather report. Field 1808 indicates a maximum storm surge time for theforensic weather report. Field 1810 indicates a maximum sustained windfor the forensic weather report. Field 1812 indicates a maximumsustained wind time for the forensic weather report. Field 1814indicates a peak wind gust speed for the forensic weather report. Field1816 indicates a peak wind gust time for the forensic weather report.

FIG. 19 is an exemplary user interface 1900 for forensic weather reportgeneration options. Field 1902 accepts a subject location. Field 1904accepts an event start time. Field 1905 accepts an event window. Theevent window is a duration of the event, typically entered in hours.Field 1906 allows a user to specify a minimum number of weather stationsto attempt to be used in selection of a forensic weather model. Field1908 allows a user to specify a maximum measurement region to be used inselection of a forensic weather model. In embodiments, the forensicweather analyzer limits the measurement region to the size specified infield 1908, even if the number of weather stations is below the valueestablished in field 1906. Field 1910 allows specification of a minimumelevation (with respect to sea level) of a weather station. Field 1912allows specification of a maximum elevation (with respect to sea level)of a weather station. Embodiments may exclude weather stations that havean elevation outside of the range specified by fields 1910 and 1912.Field 1914 is an option to allow exclusion of weather stations overlarge bodies of water such as oceans or Great Lakes. In embodiments,field 1914 may be implemented as a radio button. Field 1916 is a Savebutton, allowing the information to be saved into the memory 106 of theforensic weather analyzer 102, so that the data can be used in thegeneration of a forensic weather report such as that shown in FIG. 17.and FIG. 18. In embodiments, the user interface 1900 may be implementedvia an HTML page and rendered on a client device (such as 126 of FIG.1). In other embodiments, the user interface 1900 may be implementeddirectly by the forensic weather analyzer.

Although embodiments of the invention have been described herein assystems and method, in some embodiments, the invention may include acomputer program product. The computer program product may include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outaspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, may be non-transitory,and thus is not to be construed as being transitory signals per se, suchas radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network (for example, the Internet, a local area network, awide area network and/or a wireless network). The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device. Program data may also bereceived via the network adapter or network interface.

Computer readable program instructions for carrying out operations ofembodiments of the present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of embodiments of the present invention.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

While the invention has been particularly shown and described inconjunction with exemplary embodiments, it will be appreciated thatvariations and modifications will occur to those skilled in the art. Forexample, although the illustrative embodiments are described herein as aseries of acts or events, it will be appreciated that the presentinvention is not limited by the illustrated ordering of such acts orevents unless specifically stated. Some acts may occur in differentorders and/or concurrently with other acts or events apart from thoseillustrated and/or described herein, in accordance with the invention.In addition, not all illustrated steps may be required to implement amethodology in accordance with the present invention. Furthermore, themethods according to the present invention may be implemented inassociation with the formation and/or processing of structuresillustrated and described herein as well as in association with otherstructures not illustrated. Moreover, in particular regard to thevarious functions performed by the above described components(assemblies, devices, circuits, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (i.e., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure which performs thefunction in the herein illustrated exemplary embodiments of theinvention. In addition, while a particular feature of the invention mayhave been disclosed with respect to only one of several embodiments,such feature may be combined with one or more features of the otherembodiments as may be desired and advantageous for any given orparticular application. Therefore, it is to be understood that theappended claims are intended to cover all such modifications and changesthat fall within the true spirit of the invention.

What is claimed is:
 1. A system comprising at least one anemometer; atleast one computing device, the at least one computing device comprisinga processor, and a memory coupled to said processor, wherein the memorycontains instructions, that when executed by the processor, perform thesteps of: receiving temporal event data; receiving a subject location;generating a measurement region based on the subject location; andreceiving a plurality of meteorological measurements that are collectedwithin the measurement region; determining a station location for eachof the plurality of meteorological measurements; retrieving an estimateddata value from each of a plurality of weather models for each stationlocation based on the temporal event data; for each station location,computing a difference between a received meteorological measurement anda received estimated data value for each of the plurality of weathermodels; selecting a weather model from the plurality of weather modelsas a forensic weather model based on the computed differences; andwherein the memory further contains instructions, that when executed bythe processor, perform the steps of: generating a measurement regioncomprising a circular region of a predetermined radius, and reshapingthe measurement region based on an intersection with a geologicalboundary based on a geological exclusion zone.
 2. The system of claim 1,wherein the system further includes at least one barometer, and whereinthe memory further contains instructions, that when executed by theprocessor, perform the step of retrieving pressure data from the atleast one barometer.
 3. The system of claim 1, wherein the systemfurther includes at least one thermometer, and wherein the memoryfurther contains instructions, that when executed by the processor,perform the step of retrieving temperature data from the at least onethermometer.
 4. The system of claim 1, wherein the system furtherincludes at least one precipitation gauge, and wherein the memoryfurther contains instructions, that when executed by the processor,perform the step of retrieving precipitation data from the at least oneprecipitation gauge.
 5. The system of claim 1, wherein the memoryfurther contains instructions, that when executed by the processor,perform the steps of retrieving storm surge estimated data values from aSea, Lake, and Overland Surges from Hurricanes (SLOSH) model.
 6. Amethod comprising: receiving temporal event data; receiving a subjectlocation; generating a measurement region based on the subject location;and receiving a plurality of meteorological measurements that arecollected within the measurement region, wherein the receiving is fromat least one anemometer; determining a station location for each of theplurality of meteorological measurements; retrieving an estimated datavalue from each of the plurality of weather models for each stationlocation based on the temporal event data; for each station location,computing a difference between the meteorological measurement andestimated data value for each of the plurality of weather models; andselecting a weather model from the plurality of weather models as aforensic weather model based on the computed differences; and whereinthe memory further contains instructions, that when executed by theprocessor, perform the steps of: generating a measurement regioncomprising a circular region of a predetermined radius, and reshapingthe measurement region based on an intersection with a geologicalboundary based on a geological exclusion zone.
 7. The method of claim 6,wherein retrieving a plurality of meteorological measurements comprisesretrieving wind speed data from the at least one anemometer.
 8. Themethod of claim 6, wherein retrieving a plurality of meteorologicalmeasurements comprises retrieving pressure data from at least onebarometer.
 9. The method of claim 6, wherein retrieving a plurality ofmeteorological measurements comprises retrieving precipitation data fromat least one precipitation gauge.
 10. The method of claim 6, furthercomprising retrieving storm surge estimated data values from a Sea,Lake, and Overland Surges from Hurricanes (SLOSH) model.
 11. The methodof claim 6, wherein retrieving an estimated data value from each of theplurality of weather models for each station location based on thetemporal event data comprises retrieving a sustained wind speed value.12. The method of claim 6, wherein retrieving an estimated data valuefrom each of the plurality of weather models for each station locationbased on the temporal event data comprises retrieving a peak wind gustspeed value.
 13. The method of claim 6, wherein retrieving an estimateddata value from each of the plurality of weather models for each stationlocation based on the temporal event data comprises retrieving atemperature value.
 14. The method of claim 6, wherein retrieving anestimated data value from each of the plurality of weather models foreach station location based on the temporal event data comprisesretrieving a barometric pressure value.
 15. The method of claim 6,further comprising rendering a graph of estimated data valuescorresponding to sustained wind speed.
 16. The method of claim 15,further comprising rendering a graph of estimated data valuescorresponding to peak wind gust speed.
 17. The method of claim 16,further comprising rendering a graph of estimated storm surge datavalues.